Databases Reference
In-Depth Information
Figure A-8. A property graph is semantically fine-tuned
The property graph shown here requires several
OWNS
relationships to express what the
hypergraph captured with just one. But in using several relationships, not only are we
able to use a familiar and very explicit modeling technique, but we're also able to fine-
tune the model. For example, we've identified the “primary driver” for each vehicle (for
insurance purposes) by adding a property to the relevant relationships—something that
can't be done with a single hyper-edge.
Because hyper-edges are multidimensional, hypergraphs comprise a
more general model than property graphs. That said, the two models
are isomorphic; it is always possible to represent the information in a
hypergraph as a property graph (albeit using more relationships and
intermediary nodes). Whether a hypergraph or a property graph is best
for you is going to depend on your modeling mindset and the kinds of
applications you're building. Anecdotally, for most purposes property
graphs are widely considered to have the best balance of pragmatism
and modeling efficiency—hence their overwhelming popularity in the
graph database space. However, in situations where you need to capture
meta-intent, effectively qualifying one relationship with another (e.g.,
I like the fact that you liked that car), hypergraphs typically require fewer
primitives than property graphs.
Triples
Triple stores come from the Semantic Web movement, where researchers are interested
in large-scale knowledge inference by adding
semantic markup to the links that connect
web resources
. To date, very little of the Web has been marked up in a useful fashion,
so running queries across the semantic layer is uncommon. Instead, most effort in the
